Comparison of genotype × trait and genotype × yield-trait biplots in Sunflower cultivars

Abstract

The selection of genotypes based on various characteristics is a critical challenge in plant breeding. An experiment was carried out in a randomized complete block design (RCBD) in three replications over two crop years, 2018-2019, to compare the effects of genotype × trait (GT) and the genotype × yield-trait (GYT) methods as well as investigate the relationships between grain yield and different agronomic traits. Plant materials ten sunflowers (Helianthus annuus L.) genotypes. Based on the combined analysis of variance, there was a significant difference in parameters except leaf length. The effect of year × genotype was significant in all traits except plant height, stem diameter, and leaf length (P≤ 0.01). Gabur, Azargol and Favorite genotypes were ranked as genotypes in the years of the experiment. Based on the graphical analysis performed on the effect of genotype × trait (GT), Zaria genotype was selected as the best and stable genotype. The genotype × yield-trait (GYT) biplot ranked genotypes by yield and other desired trait levels and depicts their trait profiles, or strengths and weaknesses. The correlation biplot revealed positive correlations between most traits with grain yield. This method is pictorial, objective, effective, and simple compared to the genotype × trait (GT) method. The GYT biplot technique is based on the paradigm shift that genotypes should be assessed by their yield levels in combination with other variables rather than individually. The graphical analysis of the effect of genotype × yield-trait (GYT) revealed that Gabur and Armavirski genotypes were selected as the best for all traits investigated and could be recommended for cultivation in the Karaj location.

Identification of adaptable sunflower (Helianthus annuus L.) genotypes using yield performance and multiple-traits index

“…However, it is possible to develop genotypes with low GE interactions via sub-division of heterogeneous area into smaller, more homogeneous sub-regions; and by selecting genotypes with a better stability across a wide range of environments (Mahmodi et al, 2011;Tai, 1971). In the present study the combined ANOVA showed that, E, G and GEI explained 54.18, 16.9 and 23.50% of the total sum of squares correspondingly which is in line to the ndings of (Matta et al, 2020;Shojaeİ et al, 2023). This highest contribution of the environment con rms that sun ower genotypes might be sensitive to environmental factors and this corroborates to the ndings of (Matta et al, 2020;Van Der Merwe et al, 2013) who reported that environment is the highest contributors of the total variation in multi environment trials of sun ower.…”

Genotype by environment and genotype by yield*trait interactions in sugar beet: analyzing yield stability and determining key traits association

“…These biplot results were corroborated by the superiority index. Various studies have been conducted 24 , 36 , 70 , including the present one, which concludes that the average tester coordinate biplot is a useful method in the GYT biplot analysis. This method provides valuable information about genotypes.…”

“…Nevertheless, this does not invalidate the biplot 27 . Research by Shojaei et al 70 demonstrated that about 50% of the variation in GYT is explained by the first two PCs. In an experiment conducted by Faheem et al 36 , they estimated that the sum of the first two PCs was close to 85%, with the PC1 accounting for approximately 74% and the PC2 for around 11% of the total data variations.…”

Genotype–Trait (GT) Biplot Analysis for Yield and Quality Stability in Some Sweet Corn (Zea mays L. saccharata Sturt.) Genotypes

“…In the case of the studied traits, the number of leaves per plant (NLP) shows great sustainable stability and performance. Various researchers used polygon diagrams in order to investigate corn genotypes and evaluated traits in terms of the degree of desirability in each genotype in different years and chose this type of graph as a very widely used analysis in separating and evaluating genotypes in terms of different traits [4,7,28,29]. This genotype had a higher responsiveness to the stimuli of this set of traits because it was at the vertex of the polygon containing all the different traits [30].…”

“…Finally, in general, it can be stated that the interaction of the groups shows the similarity of the genotypes of the group in response to the traits evaluated in the experiment. Shujaei et al [28] used genotype grouping in the study of the interaction of genotype and traits (GT) as well as the genotype × trait × yield (GYD) and concluded that the groups that interact with members of other groups have similarities in term of the reaction to traits.…”